We recently released a great alphalens tutorial. While that represents the perfect introduction for analyzing factors, we are also constantly evolving our thinking and analyses. In this post, I want to give people an updated but less polished way of analyzing factors. In addition, this notebook contains some updated thoughts on what constitutes a good factor and tips on how to build it that we have not shared before. Thus, if you want to increase your chances of scoring well in the contest or getting an allocation, I think this is a good resource to study.

While these new analyses use alphalens functionality they do go beyond it. At the same time, they are much more succinct, so if before you refrained from using alphalens because it seemed daunting, check out these few plots which hopefully give you a sense of what we look for in a factor. At some point in the future, we will probably add this functionality to alphalens as well.

/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:132: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._setitem_with_indexer(indexer, value)
/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
if __name__ == '__main__':